Examining Normality in Incomplete Data Sets: A Note on a Multiple Testing Approach |
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| Authors: | Tenko Raykov Chun-Lung Lee George A Marcoulides |
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| Institution: | 1. Michigan State University;2. University of California, Santa Barbara |
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| Abstract: | A multiple testing procedure for examining the assumption of normality that is often made in analyses of incomplete data sets is outlined. The method is concerned with testing normality within each missingness pattern and arriving at an overall statement about normality using the available data. The approach is readily applied in empirical research with missing data using the popular software Mplus, Stata, and R. The procedure can be used to ascertain a main assumption underlying frequent applications of maximum likelihood in incomplete data modeling with continuous outcomes. The discussed approach is illustrated with numerical examples. |
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| Keywords: | incomplete data maximum likelihood missing at random normality |
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